Estimation of the \gamma_2 norm using an SDP

Update: This is utter bollocks, but I’ve yet to get around to correcting it.
Continuing in the vein of the previous post, we have that \gamma_2(A) \leq \|A\|_{\infty \rightarrow 1}^\star \leq K_G \gamma_2(A), so if we’re interested in approximating \|A\|_{\infty \rightarrow 1} (which looks like it’s hard to compute exactly), then we’d find it useful to be able to compute \gamma_2(A). It turns out this is easily done with an SDP when A is strictly positive:

 \min t
 \text{subject to } \begin{pmatrix} A & W_1  \\ W_2 & A^t \end{pmatrix} \succcurlyeq 0
 \quad \text{diag}(W_i) \leq t

Then t^2 = \gamma_2(A). I’m not sure what happens if A isn’t full rank, and this definitely won’t work if A is not positive semi-definite.

Possibly relevant posts:

Jan 27th, 2010 | Posted in Mathematics
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